
from __future__ import annotations

import numpy as np







def 							A__       (    lowerCamelCase    )	->						tuple[np.ndarray, np.ndarray]:
				UpperCamelCase_, UpperCamelCase_:				Dict						=    np.shape(lowerCamelCase    )
				if rows != columns:
								UpperCamelCase_:				int						=    (
								    """'table' has to be of square shaped array but got a """
								    F'''{rows}x{columns} array:\n{table}'''
								)
								raise ValueError(lowerCamelCase    )

				UpperCamelCase_:				List[Any]						=    np.zeros((rows, columns)    )
				UpperCamelCase_:				str						=    np.zeros((rows, columns)    )
				for i in range(lowerCamelCase    ):
								for j in range(lowerCamelCase    ):
												UpperCamelCase_:				Optional[int]						=    sum(lower[i][k] * upper[k][j] for k in range(lowerCamelCase    )    )
												if upper[j][j] == 0:
																raise ArithmeticError("""No LU decomposition exists"""    )
												UpperCamelCase_:				Union[str, Any]						=    (table[i][j] - total) / upper[j][j]
								UpperCamelCase_:				Any						=    1
								for j in range(lowerCamelCase  ,     lowerCamelCase    ):
												UpperCamelCase_:				Tuple						=    sum(lower[i][k] * upper[k][j] for k in range(lowerCamelCase    )    )
												UpperCamelCase_:				List[Any]						=    table[i][j] - total
				return lower, upper


if __name__ == "__main__":
							import doctest

							doctest.testmod()








import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
							lowerCamelCase_    :			int    =	argparse.ArgumentParser()

							parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")

							parser.add_argument(
							    """--txt2img_unclip""",
							    default="""kakaobrain/karlo-v1-alpha""",
							    type=str,
							    required=False,
							    help="""The pretrained txt2img unclip.""",
							)

							lowerCamelCase_    :			Dict    =	parser.parse_args()

							lowerCamelCase_    :			str    =	UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

							lowerCamelCase_    :			int    =	CLIPImageProcessor()
							lowerCamelCase_    :			Any    =	CLIPVisionModelWithProjection.from_pretrained("""openai/clip-vit-large-patch14""")

							lowerCamelCase_    :			Union[str, Any]    =	UnCLIPImageVariationPipeline(
							    decoder=txtaimg.decoder,
							    text_encoder=txtaimg.text_encoder,
							    tokenizer=txtaimg.tokenizer,
							    text_proj=txtaimg.text_proj,
							    feature_extractor=feature_extractor,
							    image_encoder=image_encoder,
							    super_res_first=txtaimg.super_res_first,
							    super_res_last=txtaimg.super_res_last,
							    decoder_scheduler=txtaimg.decoder_scheduler,
							    super_res_scheduler=txtaimg.super_res_scheduler,
							)

							imgaimg.save_pretrained(args.dump_path)






